{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "2.1_LSTM.ipynb",
      "provenance": [],
      "toc_visible": true,
      "mount_file_id": "1IG_AUpztGlas7GE4_6zDxpWq9vffu3g9",
      "authorship_tag": "ABX9TyNgURw9MFQv0YZsgdZfyeew"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "KBp1TcRyY-US"
      },
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib as mpl\n",
        "\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "\n",
        "#from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.preprocessing import MinMaxScaler"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BK5dgeE9ZOR5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "16ea23e1-c384-4608-c3ef-b4e2feb1addb"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PHg2zPK2T6qN"
      },
      "source": [
        "from psutil import * #retrieving information on running processes and system utilization (CPU, memory, disks, network, sensors)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9wfnSn1YT9ml",
        "outputId": "afe23965-61f5-44db-81e5-1e42bbb41a35"
      },
      "source": [
        "from tensorflow.python.client import device_lib\n",
        "device_lib.list_local_devices()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[name: \"/device:CPU:0\"\n",
              " device_type: \"CPU\"\n",
              " memory_limit: 268435456\n",
              " locality {\n",
              " }\n",
              " incarnation: 1096384324746844924, name: \"/device:GPU:0\"\n",
              " device_type: \"GPU\"\n",
              " memory_limit: 14638920512\n",
              " locality {\n",
              "   bus_id: 1\n",
              "   links {\n",
              "   }\n",
              " }\n",
              " incarnation: 13564136572673732469\n",
              " physical_device_desc: \"device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5\"]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jgyzQRGgNV1z"
      },
      "source": [
        "# Import data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zklVI4yPOR_s"
      },
      "source": [
        "Route 101 ***U Wandsbek Markt to S Rübenkamp***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TLSgGl3dZPUO"
      },
      "source": [
        "#path = \"/content/drive/My Drive/Colab Notebooks/Thesis/ist_0420_data_processed.csv\"\n",
        "path = \"/content/drive/My Drive/Colab Notebooks/Thesis/ist_01_12_19_data_processed.csv\"\n",
        "df_proceeded = pd.read_csv(path)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eAIXPv37ZgEn"
      },
      "source": [
        "df_proceeded"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cc-0yo2bil_d"
      },
      "source": [
        "#df_proceeded = df_proceeded.sort_values(by=['Time'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_CIQmqfYOZaw"
      },
      "source": [
        "Route 102 ***S Rübenkamp to U Wandsbek Markt***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S5OJ7Lm1Ocav"
      },
      "source": [
        "#path = \"/content/drive/My Drive/Colab Notebooks/Thesis/ist_0420_data_processed102.csv\"\n",
        "path = \"/content/drive/My Drive/Colab Notebooks/Thesis/ist_01_12_19_data_processed102.csv\"\n",
        "df_proceeded102 = pd.read_csv(path)\n",
        "#df_proceeded102 = df_proceeded102.sort_values(by=['Time'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1z64dy9EOeru"
      },
      "source": [
        "Route 201 ***U Wandsbek Markt to Lufthansa-Basis (Haupteingang)***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_3Bi66sdOhIL"
      },
      "source": [
        "#path = \"/content/drive/My Drive/Colab Notebooks/Thesis/ist_0420_data_processed201.csv\"\n",
        "path = \"/content/drive/My Drive/Colab Notebooks/Thesis/ist_01_12_19_data_processed201.csv\"\n",
        "df_proceeded201 = pd.read_csv(path)\n",
        "#df_proceeded201 = df_proceeded201.sort_values(by=['Time'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VxEiR1W8OkJB"
      },
      "source": [
        "Route 202 ***Lufthansa-Basis (Haupteingang) to U Wandsbek Markt***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sZdc12QvOn5h"
      },
      "source": [
        "#path = \"/content/drive/My Drive/Colab Notebooks/Thesis/ist_0420_data_processed202.csv\"\n",
        "path = \"/content/drive/My Drive/Colab Notebooks/Thesis/ist_01_12_19_data_processed202.csv\"\n",
        "df_proceeded202 = pd.read_csv(path)\n",
        "#df_proceeded202 = df_proceeded202.sort_values(by=['Time'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YTty1BPGeuSL"
      },
      "source": [
        "## **Preprocessing**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dShXRO_vOy3Y"
      },
      "source": [
        "Route 101 ***U Wandsbek Markt to S Rübenkamp***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eDs6hC7bZiAO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9313a7bf-e04c-4709-ce06-f0d47a607e2d"
      },
      "source": [
        "data = df_proceeded[['Node A to Node B', 'Precipitation', 'dayofweek', 'timeofday', 'Driving_time_s']]\n",
        "data.index = df_proceeded['Time']\n",
        "data['Precipitation'] = data['Precipitation'].astype('int')\n",
        "data['timeofday'] = data['timeofday'].astype('int')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  after removing the cwd from sys.path.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "idrT_5hSe_ix"
      },
      "source": [
        "data"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zLiw2wEdcRyU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a79cdc62-78b2-4dff-8a7c-ed001d2bb1cd"
      },
      "source": [
        "columns_to_category = ['Node A to Node B', 'Precipitation', 'dayofweek', 'timeofday']\n",
        "data[columns_to_category] = data[columns_to_category].astype('category') # change datetypes to category"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pandas/core/frame.py:3069: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self[k1] = value[k2]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wUdJ_TvledAy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f72d284c-47a6-41bf-e898-1fd6208e2363"
      },
      "source": [
        "data.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Node A to Node B    category\n",
              "Precipitation       category\n",
              "dayofweek           category\n",
              "timeofday           category\n",
              "Driving_time_s       float64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hP7fpPv5gGiW"
      },
      "source": [
        "## One-hot encoding"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ngmjPjYBgSGb"
      },
      "source": [
        "One-hot encode the categorical feautures.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YyXqx2CGfxN4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d4c6ca26-001f-4814-9d3d-aa7bbba0cf43"
      },
      "source": [
        "data = pd.get_dummies(data, columns=columns_to_category) # One-hot encoding the categories\n",
        "data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Driving_time_s</th>\n",
              "      <th>Node A to Node B_AK Barmbek|Hebebrandstraße</th>\n",
              "      <th>Node A to Node B_Habichtsplatz|AK Barmbek</th>\n",
              "      <th>Node A to Node B_Habichtsplatz|Hartzloh</th>\n",
              "      <th>Node A to Node B_Habichtsplatz|Neue Wöhr</th>\n",
              "      <th>Node A to Node B_Habichtstraße (Mitte)|Habichtsplatz</th>\n",
              "      <th>Node A to Node B_Habichtstraße (Mitte)|U Habichtstraße</th>\n",
              "      <th>Node A to Node B_Hartzloh|AK Barmbek</th>\n",
              "      <th>Node A to Node B_Hartzloh|Hebebrandstraße</th>\n",
              "      <th>Node A to Node B_Hebebrandstraße|S Rübenkamp</th>\n",
              "      <th>Node A to Node B_Lämmersieth (Nord)|Habichtstraße (Mitte)</th>\n",
              "      <th>Node A to Node B_Neue Wöhr|Hartzloh</th>\n",
              "      <th>Node A to Node B_Neue Wöhr|S Rübenkamp</th>\n",
              "      <th>Node A to Node B_S Rübenkamp|Source</th>\n",
              "      <th>Node A to Node B_U Alter Teichweg|Habichtstraße (Mitte)</th>\n",
              "      <th>Node A to Node B_U Alter Teichweg|Lämmersieth (Nord)</th>\n",
              "      <th>Node A to Node B_U Alter Teichweg|U Habichtstraße</th>\n",
              "      <th>Node A to Node B_U Habichtstraße|Habichtsplatz</th>\n",
              "      <th>Node A to Node B_U Straßburger Straße|Habichtstraße (Mitte)</th>\n",
              "      <th>Node A to Node B_U Straßburger Straße|U Alter Teichweg</th>\n",
              "      <th>Node A to Node B_U Wandsbek Markt|Habichtsplatz</th>\n",
              "      <th>Node A to Node B_U Wandsbek Markt|Habichtstraße (Mitte)</th>\n",
              "      <th>Node A to Node B_U Wandsbek Markt|Neue Wöhr</th>\n",
              "      <th>Node A to Node B_U Wandsbek Markt|S Rübenkamp</th>\n",
              "      <th>Node A to Node B_U Wandsbek Markt|U Alter Teichweg</th>\n",
              "      <th>Node A to Node B_U Wandsbek Markt|U Habichtstraße</th>\n",
              "      <th>Node A to Node B_U Wandsbek Markt|U Straßburger Straße</th>\n",
              "      <th>Node A to Node B_U Wandsbek Markt|Wandsbeker Allee</th>\n",
              "      <th>Node A to Node B_Wandsbeker Allee|U Alter Teichweg</th>\n",
              "      <th>Node A to Node B_Wandsbeker Allee|U Straßburger Straße</th>\n",
              "      <th>Precipitation_-499</th>\n",
              "      <th>Precipitation_0</th>\n",
              "      <th>Precipitation_1</th>\n",
              "      <th>dayofweek_0</th>\n",
              "      <th>dayofweek_1</th>\n",
              "      <th>dayofweek_2</th>\n",
              "      <th>dayofweek_3</th>\n",
              "      <th>dayofweek_4</th>\n",
              "      <th>dayofweek_5</th>\n",
              "      <th>dayofweek_6</th>\n",
              "      <th>timeofday_0</th>\n",
              "      <th>timeofday_1</th>\n",
              "      <th>timeofday_2</th>\n",
              "      <th>timeofday_3</th>\n",
              "      <th>timeofday_4</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Time</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2019-01-02 09:47:00</th>\n",
              "      <td>204.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-02 09:50:00</th>\n",
              "      <td>108.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-02 09:52:00</th>\n",
              "      <td>96.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-02 09:54:00</th>\n",
              "      <td>168.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-02 09:57:00</th>\n",
              "      <td>54.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     Driving_time_s  ...  timeofday_4\n",
              "Time                                 ...             \n",
              "2019-01-02 09:47:00           204.0  ...            0\n",
              "2019-01-02 09:50:00           108.0  ...            0\n",
              "2019-01-02 09:52:00            96.0  ...            0\n",
              "2019-01-02 09:54:00           168.0  ...            0\n",
              "2019-01-02 09:57:00            54.0  ...            0\n",
              "\n",
              "[5 rows x 45 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NqNZvpJj3Ysa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "221da165-41b0-4cf6-ee46-af941f4aaa73"
      },
      "source": [
        "data.describe"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method NDFrame.describe of                      Driving_time_s  ...  timeofday_4\n",
              "Time                                 ...             \n",
              "2019-01-02 09:47:00           204.0  ...            0\n",
              "2019-01-02 09:50:00           108.0  ...            0\n",
              "2019-01-02 09:52:00            96.0  ...            0\n",
              "2019-01-02 09:54:00           168.0  ...            0\n",
              "2019-01-02 09:57:00            54.0  ...            0\n",
              "...                             ...  ...          ...\n",
              "2019-12-23 08:58:00           144.0  ...            0\n",
              "2019-12-23 09:01:00            66.0  ...            0\n",
              "2019-12-23 09:02:00            60.0  ...            0\n",
              "2019-12-23 09:03:00           132.0  ...            0\n",
              "2019-12-23 09:05:00            60.0  ...            0\n",
              "\n",
              "[133274 rows x 45 columns]>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MGvKCxKSO9Zv"
      },
      "source": [
        "data101 = data.copy()\n",
        "data101"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lEUSFuXnPnVj"
      },
      "source": [
        "## Preprocessing with other routes (journeys)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zoXrAJDkPBPb"
      },
      "source": [
        "Journey 102 ***S Rübenkamp to U Wandsbek Markt***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yMN02Xn0PL5I",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "729966ee-3d08-4f3a-9e0f-d1af86364774"
      },
      "source": [
        "data = df_proceeded102[['Node A to Node B', 'Precipitation', 'dayofweek', 'timeofday', 'Driving_time_s']]\n",
        "data.index = df_proceeded102['Time']\n",
        "data['Precipitation'] = data['Precipitation'].astype('int')\n",
        "data['timeofday'] = data['timeofday'].astype('int')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  after removing the cwd from sys.path.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4WhJQMgeL5pt",
        "outputId": "4778ab4b-9ce7-4b2e-c383-c05aec8b17eb"
      },
      "source": [
        "data.loc[data.Precipitation == -499 ]=0\n",
        "data.loc[data.Precipitation == -499 ]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py:1763: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  isetter(loc, value)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Node A to Node B</th>\n",
              "      <th>Precipitation</th>\n",
              "      <th>dayofweek</th>\n",
              "      <th>timeofday</th>\n",
              "      <th>Driving_time_s</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Time</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [Node A to Node B, Precipitation, dayofweek, timeofday, Driving_time_s]\n",
              "Index: []"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aMoESyWMMnLm"
      },
      "source": [
        "data = data.loc[data['Node A to Node B'].isin(['U Straßburger Straße|Wandsbeker Allee',\n",
        "'AK Barmbek|Hartzloh',\n",
        "'U Habichtstraße|Habichtstraße (Mitte)',\n",
        "'Habichtsplatz|U Habichtstraße',\n",
        "'U Alter Teichweg|U Straßburger Straße',\n",
        "'Wandsbeker Allee|U Wandsbek Markt',\n",
        "'Hartzloh|Habichtsplatz',\n",
        "'S Rübenkamp|AK Barmbek',\n",
        "'Habichtstraße (Mitte)|U Alter Teichweg'])]\n",
        "df_proceeded102 = data"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5voDsmlDPNLc"
      },
      "source": [
        "columns_to_category = ['Node A to Node B', 'Precipitation', 'dayofweek', 'timeofday']\n",
        "data[columns_to_category] = data[columns_to_category].astype('category') # change datetypes to category"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5CNHFnEBPPyC"
      },
      "source": [
        "data = pd.get_dummies(data, columns=columns_to_category) # One hot encoding the categories\n",
        "data[:50]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nxtY2PbMPSbt"
      },
      "source": [
        "data102 = data.copy()\n",
        "data102"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tcrJ3n7fPVey"
      },
      "source": [
        "Route 201 ***U Wandsbek Markt to Lufthansa-Basis (Haupteingang)***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xw6FHyowPphg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "109cdbac-3cb4-4336-f732-66f08b389843"
      },
      "source": [
        "data = df_proceeded201[['Node A to Node B', 'Precipitation', 'dayofweek', 'timeofday', 'Driving_time_s']]\n",
        "data.index = df_proceeded201['Time']\n",
        "data['Precipitation'] = data['Precipitation'].astype('int')\n",
        "data['timeofday'] = data['timeofday'].astype('int')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  after removing the cwd from sys.path.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jDXMMhmjMx9I",
        "outputId": "61c10efd-8910-4b2b-e29c-007e14d92b18"
      },
      "source": [
        "data.loc[data.Precipitation == -499 ]=0\n",
        "data.loc[data.Precipitation == -499 ]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py:1763: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  isetter(loc, value)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Node A to Node B</th>\n",
              "      <th>Precipitation</th>\n",
              "      <th>dayofweek</th>\n",
              "      <th>timeofday</th>\n",
              "      <th>Driving_time_s</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Time</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [Node A to Node B, Precipitation, dayofweek, timeofday, Driving_time_s]\n",
              "Index: []"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9oCb5ks3C5BI"
      },
      "source": [
        "data = data.loc[data['Node A to Node B'].isin(['Kapstadtring|Manilabrücke',\n",
        "'U Alsterdorf|Hindenburgstraße',\n",
        "'Habichtstraße (Mitte)|U Habichtstraße',\n",
        "'Neue Wöhr|Hartzloh',\n",
        "'Hartzloh|AK Barmbek',\n",
        "'Brabandstraße|Moltrechtweg',\n",
        "'Alsterkrugchaussee (Mitte)|Obenhauptstraße',\n",
        "'Manilabrücke|Sydneystraße',\n",
        "'Hebebrandstraße|S Rübenkamp',\n",
        "'U Habichtstraße|Habichtsplatz',\n",
        "'Moltrechtweg|Alsterkrugchaussee (Mitte)',\n",
        "'Hindenburgstraße|Brabandstraße',\n",
        "'S Rübenkamp|Kapstadtring',\n",
        "'Wandsbeker Allee|U Straßburger Straße',\n",
        "'Sydneystraße|U Alsterdorf',\n",
        "'Habichtsplatz|Neue Wöhr',\n",
        "'AK Barmbek|Hebebrandstraße',\n",
        "'U Straßburger Straße|U Alter Teichweg',\n",
        "'Obenhauptstraße|Lufthansa-Basis (Haupteingang)',\n",
        "'U Alter Teichweg|Habichtstraße (Mitte)',\n",
        "'U Wandsbek Markt|Wandsbeker Allee',\n",
        "])]\n",
        "df_proceeded201 = data"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TJCYBpJJPwGT"
      },
      "source": [
        "columns_to_category = ['Node A to Node B', 'Precipitation', 'dayofweek', 'timeofday']\n",
        "data[columns_to_category] = data[columns_to_category].astype('category') # change datetypes to category"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8cOGuVMOP1xG"
      },
      "source": [
        "data = pd.get_dummies(data, columns=columns_to_category) # One hot encoding the categories\n",
        "data[:50]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V7vEjg1uP4yW"
      },
      "source": [
        "data201 = data.copy()\n",
        "data201"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PBLdO4NQP56n"
      },
      "source": [
        "Route 202 ***Lufthansa-Basis (Haupteingang) to U Wandsbek Markt***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_nPGb_2fP_2o",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "99bca99d-4d20-48e9-d9b1-20d11f2a9d61"
      },
      "source": [
        "data = df_proceeded202[['Node A to Node B', 'Precipitation', 'dayofweek', 'timeofday', 'Driving_time_s']]\n",
        "data.index = df_proceeded202['Time']\n",
        "data['Precipitation'] = data['Precipitation'].astype('int')\n",
        "data['timeofday'] = data['timeofday'].astype('int')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  after removing the cwd from sys.path.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T3Fc7VrFDiwi",
        "outputId": "93f32a77-0f72-4f35-de33-354f54a5a881"
      },
      "source": [
        "data.loc[data.Precipitation == -499 ]=0\n",
        "data.loc[data.Precipitation == -499 ]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py:1763: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  isetter(loc, value)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Node A to Node B</th>\n",
              "      <th>Precipitation</th>\n",
              "      <th>dayofweek</th>\n",
              "      <th>timeofday</th>\n",
              "      <th>Driving_time_s</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Time</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [Node A to Node B, Precipitation, dayofweek, timeofday, Driving_time_s]\n",
              "Index: []"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dNC2FLxSDlql"
      },
      "source": [
        "data = data.loc[data['Node A to Node B'].isin(['Lufthansa-Basis (Haupteingang)|Obenhauptstraße',\n",
        "        'Obenhauptstraße|Alsterkrugchaussee (Mitte)',\n",
        "        'Alsterkrugchaussee (Mitte)|Moltrechtweg',\n",
        "        'Moltrechtweg|Brabandstraße', 'Brabandstraße|Hindenburgstraße',\n",
        "        'Hindenburgstraße|U Alsterdorf', 'U Alsterdorf|Sydneystraße',\n",
        "        'Sydneystraße|Manilabrücke', 'Manilabrücke|Kapstadtring',\n",
        "        'Kapstadtring|S Rübenkamp', 'S Rübenkamp|AK Barmbek', 'AK Barmbek|Hartzloh',\n",
        "        'Hartzloh|Habichtsplatz', 'Habichtsplatz|U Habichtstraße',\n",
        "        'U Habichtstraße|Habichtstraße (Mitte)',\n",
        "        'Habichtstraße (Mitte)|U Alter Teichweg',\n",
        "        'U Alter Teichweg|U Straßburger Straße',\n",
        "        'U Straßburger Straße|Wandsbeker Allee',\n",
        "        'Wandsbeker Allee|U Wandsbek Markt'])]\n",
        "df_proceeded202 = data"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KRW5qSTnQBKq"
      },
      "source": [
        "columns_to_category = ['Node A to Node B', 'Precipitation', 'dayofweek', 'timeofday']\n",
        "data[columns_to_category] = data[columns_to_category].astype('category') # change datetypes to category"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yGKpSFYaQNar"
      },
      "source": [
        "data = pd.get_dummies(data, columns=columns_to_category) # One hot encoding the categories\n",
        "data[:50]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rX0K6hlXQDyi"
      },
      "source": [
        "data202 = data.copy()\n",
        "data202"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TWr1tngbQS9t"
      },
      "source": [
        "# Select route to implement"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "27vcUnOvQWAr"
      },
      "source": [
        "#select the route one by one\n",
        "route = 202\n",
        "month = 4\n",
        "\n",
        "if route == 101:\n",
        "  #route 101\n",
        "  data = data101.copy()\n",
        "  path_end = \"data101\"\n",
        "  path_name = \"Route 101 U Wandsbek Markt to S Rübenkamp\"\n",
        "  bs = 2**4\n",
        "  bs_name = str(bs)\n",
        "  n_layers = 3\n",
        "\n",
        "elif route ==102:\n",
        "  #route 102\n",
        "  data = data102.copy()\n",
        "  path_end = \"data102\"\n",
        "  path_name = \"Route 102 S Rübenkamp to U Wandsbek Markt\"\n",
        "  bs = 2**7\n",
        "  bs_name = str(bs)\n",
        "  n_layers = 2\n",
        "\n",
        "elif route == 201:\n",
        "  #route 201\n",
        "  data = data201.copy()\n",
        "  path_end = \"data201\"\n",
        "  path_name = \"Route 201 U Wandsbek Markt to Lufthansa-Basis (Haupteingang)\"\n",
        "  bs = 2**6\n",
        "  bs_name = str(bs)\n",
        "  n_layers = 2\n",
        "\n",
        "elif route == 202:\n",
        "#route 202\n",
        "  data = data202.copy()\n",
        "  path_end = \"data202\"\n",
        "  path_name = \"Route 202 Lufthansa-Basis (Haupteingang) to U Wandsbek Markt\"\n",
        "  bs = 2**6\n",
        "  bs_name = str(bs)\n",
        "  n_layers = 3\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6sA-FiunP1l0"
      },
      "source": [
        "data = data.loc[pd.to_datetime(data.index).month == month]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P7MA4pvra7yU"
      },
      "source": [
        "#Import data to collect results and compare\n",
        "#comb_result = pd.read_csv(\"/content/drive/My Drive/Colab Notebooks/Thesis/combine_result_\"+path_end+\".csv\")\n",
        "#comb_result_rmse = pd.read_csv(\"/content/drive/My Drive/Colab Notebooks/Thesis/combine_result_rmse_\"+path_end+\".csv\")\n",
        "comb_result = pd.read_csv(\"/content/drive/My Drive/Colab Notebooks/Thesis/combine_result_\"+path_end+\"_m\"+str(month)+\".csv\")\n",
        "#comb_result_rmse = pd.read_csv(\"/content/drive/My Drive/Colab Notebooks/Thesis/combine_result_rmse_\"+path_end+\"_m\"+str(month)+\".csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SMnb7iePbJUX"
      },
      "source": [
        "size_trip = len(comb_result['Path'].unique())\n",
        "#size_trip = 1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t4FFZGHRQnde"
      },
      "source": [
        "# Select model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d5qdIctsQtZd"
      },
      "source": [
        "# set variable for RNN structure\n",
        "look_back = size_trip*1 #1*24 # D -> 1, H -> 5*24\n",
        "num_features = data.shape[1] - 1\n",
        "output_dim = 1\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XaLg54F00aHr"
      },
      "source": [
        "# Data Preprocessing to RNN format"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lhmR6mEMXpiz"
      },
      "source": [
        "\"The LSTM network expects the input data (X) to be provided with a specific array structure in the form of: [samples, time steps, features].\"\n",
        "\n",
        "https://machinelearningmastery.com/time-series-prediction-lstm-recurrent-neural-networks-python-keras/"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aXKas5PI04X9"
      },
      "source": [
        "def df_to_rnn_scaling(df, look_back=5, target_column='target', scale_X=True): #, test_size=0.5\n",
        "    df = df.copy()\n",
        "\n",
        "    # move traget function to last column of the dataframe\n",
        "    df['target'] = df[target_column] # Make a copy of the target column to the last column\n",
        "    df = df.drop(columns=[target_column]) # Drop the original target column\n",
        "    \n",
        "    target_location = df.shape[1] - 1 # column index number of target\n",
        "\n",
        "    \n",
        "    #Split with day of the month\n",
        "    s =pd.to_datetime(data.index).day.tolist()\n",
        "\n",
        "    split_index_val = s.index(15) #validation set is cover on 3th week\n",
        "    split_index_test = s.index(23) #test set covers last week\n",
        "    #split_index = int(df.shape[0]*test_size) # the index at which to split df into train and test\n",
        "    \n",
        "    train = df[:split_index_val]\n",
        "    val = df[split_index_val:split_index_test]\n",
        "    test = df[split_index_test:]\n",
        "   \n",
        "    # features scaling\n",
        "    scaler = MinMaxScaler()\n",
        "    # fit scaler\n",
        "    scaler.fit(train)\n",
        "    # transform training dataset\n",
        "    train = scaler.transform(train)\n",
        "    # transform validation dataset\n",
        "    val = scaler.transform(val)\n",
        "    # transform test dataset\n",
        "    test = scaler.transform(test)\n",
        "\n",
        "    # ...train\n",
        "    X_train = train[:, :target_location]\n",
        "    y_train = train[:, target_location]\n",
        "\n",
        "    # ...validation\n",
        "    X_val = val[:, :target_location]\n",
        "    y_val = val[:, target_location]\n",
        "\n",
        "    # ...test\n",
        "    X_test = test[:, :target_location]\n",
        "    y_test = test[:, target_location] \n",
        "        \n",
        "    # Reshape the arrays\n",
        "    num_features = target_location # All columns before the target column are features\n",
        "\n",
        "    # train\n",
        "    samples_train = X_train.shape[0] - look_back\n",
        "    X_train_reshaped = np.zeros((samples_train, look_back, num_features)) # Initialize the required dataframe with an zeros array.\n",
        "    y_train_reshaped = np.zeros((samples_train))\n",
        "\n",
        "    for i in range(samples_train):\n",
        "        y_position = i + look_back\n",
        "        X_train_reshaped[i] = X_train[i:y_position]\n",
        "        y_train_reshaped[i] = y_train[y_position]\n",
        "\n",
        "    # validation\n",
        "    samples_val = X_val.shape[0] - look_back\n",
        "    X_val_reshaped = np.zeros((samples_val, look_back, num_features))\n",
        "    y_val_reshaped = np.zeros((samples_val))\n",
        "\n",
        "    for i in range(samples_val):\n",
        "        y_position = i + look_back\n",
        "        X_val_reshaped[i] = X_val[i:y_position]\n",
        "        y_val_reshaped[i] = y_val[y_position]\n",
        "\n",
        "    # test\n",
        "    samples_test = X_test.shape[0] - look_back\n",
        "    X_test_reshaped = np.zeros((samples_test, look_back, num_features))\n",
        "    y_test_reshaped = np.zeros((samples_test))\n",
        "\n",
        "    for i in range(samples_test):\n",
        "        y_position = i + look_back\n",
        "        X_test_reshaped[i] = X_test[i:y_position]\n",
        "        y_test_reshaped[i] = y_test[y_position]\n",
        "    \n",
        "    return X_train_reshaped, y_train_reshaped, X_val_reshaped, y_val_reshaped, X_test_reshaped, y_test_reshaped, split_index_test, scaler"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SFFvVhA47SBl"
      },
      "source": [
        "X_train, y_train, X_val, y_val, X_test, y_test, split_index_test, scaler = df_to_rnn_scaling(df=data, look_back=look_back, target_column='Driving_time_s', scale_X=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6pcU1kHX8ZFR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "650bb361-2358-4b6a-c151-47ff2550e109"
      },
      "source": [
        "print('X_train_shape: ', X_train.shape,' ', 'X_test_shape: ', X_test.shape)\n",
        "print('y_train_shape: ', y_train.shape,' ', 'y_test_shape: ', y_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "X_train_shape:  (9046, 19, 32)   X_test_shape:  (1422, 19, 32)\n",
            "y_train_shape:  (9046,)   y_test_shape:  (1422,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "feIvsKi2Tfeq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "75b511f9-3295-461b-cad2-2f9183b43734"
      },
      "source": [
        "date_time = pd.to_datetime(data.index)\n",
        "#plot_cols = ['T (degC)', 'p (mbar)', 'rho (g/m**3)']\n",
        "plot_features = data['Driving_time_s']\n",
        "plot_features.index = date_time\n",
        "_ = plot_features.plot()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y6VYHgkItcXb"
      },
      "source": [
        "# LSTM model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GaHBG9ECuror",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3d8f929c-39fc-48c2-963f-fea31b0465b8"
      },
      "source": [
        "!pip show keras"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Name: Keras\n",
            "Version: 2.4.3\n",
            "Summary: Deep Learning for humans\n",
            "Home-page: https://github.com/keras-team/keras\n",
            "Author: Francois Chollet\n",
            "Author-email: francois.chollet@gmail.com\n",
            "License: MIT\n",
            "Location: /usr/local/lib/python3.6/dist-packages\n",
            "Requires: pyyaml, h5py, scipy, numpy\n",
            "Required-by: textgenrnn, keras-vis, kapre, fancyimpute\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OwCYWdLS_OOX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9639e42d-cfe9-42b2-a8ef-d76c33426041"
      },
      "source": [
        "# TensorFlow ≥2.0 is required\n",
        "import tensorflow as tf\n",
        "assert tf.__version__ >= \"2.0\"\n",
        "\n",
        "from tensorflow import keras\n",
        "from keras.layers import LeakyReLU\n",
        "from keras.layers.normalization import BatchNormalization\n",
        "keras.backend.clear_session()\n",
        "from keras.optimizers import Adam, SGD, Nadam\n",
        "!pip install livelossplot\n",
        "from livelossplot import PlotLossesKeras\n",
        "#from keras.experimental import CosineDecayRestarts"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: livelossplot in /usr/local/lib/python3.6/dist-packages (0.5.3)\n",
            "Requirement already satisfied: matplotlib; python_version >= \"3.6\" in /usr/local/lib/python3.6/dist-packages (from livelossplot) (3.2.2)\n",
            "Requirement already satisfied: ipython in /usr/local/lib/python3.6/dist-packages (from livelossplot) (5.5.0)\n",
            "Requirement already satisfied: bokeh; python_version >= \"3.6\" in /usr/local/lib/python3.6/dist-packages (from livelossplot) (2.1.1)\n",
            "Requirement already satisfied: numpy>=1.11 in /usr/local/lib/python3.6/dist-packages (from matplotlib; python_version >= \"3.6\"->livelossplot) (1.19.5)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib; python_version >= \"3.6\"->livelossplot) (0.10.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib; python_version >= \"3.6\"->livelossplot) (2.4.7)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib; python_version >= \"3.6\"->livelossplot) (2.8.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib; python_version >= \"3.6\"->livelossplot) (1.3.1)\n",
            "Requirement already satisfied: prompt-toolkit<2.0.0,>=1.0.4 in /usr/local/lib/python3.6/dist-packages (from ipython->livelossplot) (1.0.18)\n",
            "Requirement already satisfied: decorator in /usr/local/lib/python3.6/dist-packages (from ipython->livelossplot) (4.4.2)\n",
            "Requirement already satisfied: setuptools>=18.5 in /usr/local/lib/python3.6/dist-packages (from ipython->livelossplot) (51.1.1)\n",
            "Requirement already satisfied: simplegeneric>0.8 in /usr/local/lib/python3.6/dist-packages (from ipython->livelossplot) (0.8.1)\n",
            "Requirement already satisfied: traitlets>=4.2 in /usr/local/lib/python3.6/dist-packages (from ipython->livelossplot) (4.3.3)\n",
            "Requirement already satisfied: pexpect; sys_platform != \"win32\" in /usr/local/lib/python3.6/dist-packages (from ipython->livelossplot) (4.8.0)\n",
            "Requirement already satisfied: pygments in /usr/local/lib/python3.6/dist-packages (from ipython->livelossplot) (2.6.1)\n",
            "Requirement already satisfied: pickleshare in /usr/local/lib/python3.6/dist-packages (from ipython->livelossplot) (0.7.5)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4 in /usr/local/lib/python3.6/dist-packages (from bokeh; python_version >= \"3.6\"->livelossplot) (3.7.4.3)\n",
            "Requirement already satisfied: pillow>=4.0 in /usr/local/lib/python3.6/dist-packages (from bokeh; python_version >= \"3.6\"->livelossplot) (7.0.0)\n",
            "Requirement already satisfied: PyYAML>=3.10 in /usr/local/lib/python3.6/dist-packages (from bokeh; python_version >= \"3.6\"->livelossplot) (3.13)\n",
            "Requirement already satisfied: Jinja2>=2.7 in /usr/local/lib/python3.6/dist-packages (from bokeh; python_version >= \"3.6\"->livelossplot) (2.11.2)\n",
            "Requirement already satisfied: tornado>=5.1 in /usr/local/lib/python3.6/dist-packages (from bokeh; python_version >= \"3.6\"->livelossplot) (5.1.1)\n",
            "Requirement already satisfied: packaging>=16.8 in /usr/local/lib/python3.6/dist-packages (from bokeh; python_version >= \"3.6\"->livelossplot) (20.8)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from cycler>=0.10->matplotlib; python_version >= \"3.6\"->livelossplot) (1.15.0)\n",
            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.6/dist-packages (from prompt-toolkit<2.0.0,>=1.0.4->ipython->livelossplot) (0.2.5)\n",
            "Requirement already satisfied: ipython-genutils in /usr/local/lib/python3.6/dist-packages (from traitlets>=4.2->ipython->livelossplot) (0.2.0)\n",
            "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.6/dist-packages (from pexpect; sys_platform != \"win32\"->ipython->livelossplot) (0.7.0)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.6/dist-packages (from Jinja2>=2.7->bokeh; python_version >= \"3.6\"->livelossplot) (1.1.1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JXfRDK8yqZHY"
      },
      "source": [
        "# to make output stable across runs\n",
        "np.random.seed(42)\n",
        "tf.random.set_seed(42)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1BXQ4fYaGU4l"
      },
      "source": [
        "from keras.callbacks import EarlyStopping, TensorBoard, ModelCheckpoint\n",
        "\n",
        "callback = keras.callbacks.EarlyStopping(monitor='val_loss', patience=5)\n",
        "\n",
        "checkpoint1 = ModelCheckpoint(\"/content/drive/My Drive/Colab Notebooks/Thesis/best model/LSTM.best_mse_\"+path_end+bs_name+\".hdf5\", monitor='val_loss', verbose=1, save_best_only=True, mode='min')\n",
        "checkpoint2 = ModelCheckpoint(\"/content/drive/My Drive/Colab Notebooks/Thesis/best model/LSTM.best_rmse_\"+path_end+bs_name+\".hdf5\", monitor='val_root_mean_squared_error', verbose=1, save_best_only=True, mode='min')\n",
        "checkpoint3 = ModelCheckpoint(\"/content/drive/My Drive/Colab Notebooks/Thesis/best model/LSTM.best_mae_\"+path_end+bs_name+\".hdf5\", monitor='val_mae', verbose=1, save_best_only=True, mode='min')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zHrjrugr1gNp"
      },
      "source": [
        "LSTM model\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NdeqWRzNzV1I"
      },
      "source": [
        "#crate model\n",
        "model = keras.models.Sequential() \n",
        "model.add(BatchNormalization(name = 'batch_norm_0', input_shape = (look_back, num_features)))\n",
        "#Hidden Layer 1\n",
        "model.add(keras.layers.LSTM(16, name = 'lstm_1', return_sequences=True, kernel_initializer='TruncatedNormal'))#, activation='relu'))\n",
        "model.add(LeakyReLU())\n",
        "model.add(keras.layers.Dropout(0.2, name = 'dropout_1'))\n",
        "model.add(BatchNormalization(name = 'batch_norm_1'))\n",
        "\n",
        "# Adds remaining hidden layers\n",
        "for i in range(2, n_layers + 1):\n",
        "    if i == n_layers: #the last hidden layer\n",
        "        model.add(keras.layers.LSTM(16, name = 'lstm_{}'.format(i), return_sequences=False, kernel_initializer='TruncatedNormal', use_bias=False, activation='relu'))\n",
        "        model.add(keras.layers.Dropout(0.2, name = 'dropout_{}'.format(i)))\n",
        "        model.add(BatchNormalization(name = 'batch_norm_{}'.format(i)))\n",
        "    else:\n",
        "        model.add(keras.layers.LSTM(16, name = 'lstm_{}'.format(i), return_sequences=True, kernel_initializer='TruncatedNormal', use_bias=False)) #16*(2**i)\n",
        "                        #name='h{}'.format(i)))#, activation='relu'))\n",
        "        model.add(LeakyReLU())\n",
        "        model.add(keras.layers.Dropout(0.2, name = 'dropout_{}'.format(i)))\n",
        "        model.add(BatchNormalization(name = 'batch_norm_{}'.format(i)))\n",
        "\n",
        "#Layer output\n",
        "model.add(keras.layers.Dense(1))\n",
        "\n",
        "\n",
        "\n",
        "'''\n",
        "lr = tf.keras.experimental.CosineDecayRestarts(\n",
        "    initial_learning_rate=0.0005, first_decay_steps=300, t_mul=2.0, m_mul=1.0, alpha=0.0,\n",
        "    name=None\n",
        ")\n",
        "'''\n",
        "lr=1.e-3\n",
        "'''\n",
        "n_steps=2000\n",
        "global_step=1\n",
        "LR = keras.experimental.CosineDecayRestarts(\n",
        "    initial_learning_rate=lr,\n",
        "    first_decay_steps=n_steps,\n",
        "    t_mul= 1.5,\n",
        "    m_mul= 1,\n",
        "    alpha=0.1,\n",
        "    name=None\n",
        ")\n",
        "'''\n",
        "adam = Adam(learning_rate=lr) #, weight_decay=1e-5)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W5czarb27807",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "d63f9cd9-27cd-436d-ddbc-6774572d3981"
      },
      "source": [
        "model.compile(loss=\"mean_squared_error\", optimizer=adam, metrics=['RootMeanSquaredError', 'mae'])\n",
        "\n",
        "\n",
        "'''\n",
        "schedule = SGDRScheduler(min_lr=7.7e-6, #1e-5\n",
        "                                     max_lr=2.9e-2, # 1e-2\n",
        "                                     steps_per_epoch=np.ceil(1/2**13),\n",
        "                                     lr_decay=0.9,\n",
        "                                     cycle_length=25, # 5\n",
        "                                     mult_factor=1.5)\n",
        "'''\n",
        "history = model.fit(X_train, y_train, epochs=50, batch_size=32, callbacks=[callback, checkpoint1, checkpoint2, checkpoint3, PlotLossesKeras()],\n",
        "                   validation_data=(X_val, y_val))\n",
        "#mse_test = model.evaluate(X_test, y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x864 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Loss\n",
            "\ttraining         \t (min:    0.005, max:    0.542, cur:    0.005)\n",
            "\tvalidation       \t (min:    0.005, max:    0.015, cur:    0.005)\n",
            "mae\n",
            "\ttraining         \t (min:    0.049, max:    0.486, cur:    0.049)\n",
            "\tvalidation       \t (min:    0.046, max:    0.092, cur:    0.046)\n",
            "root_mean_squared_error\n",
            "\ttraining         \t (min:    0.068, max:    0.736, cur:    0.069)\n",
            "\tvalidation       \t (min:    0.070, max:    0.122, cur:    0.070)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dVGvOwlP8IGZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f4f2691c-996d-4966-94b4-5504345193c8"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "batch_norm_0 (BatchNormaliza (None, 19, 32)            128       \n",
            "_________________________________________________________________\n",
            "lstm_1 (LSTM)                (None, 19, 32)            8320      \n",
            "_________________________________________________________________\n",
            "leaky_re_lu (LeakyReLU)      (None, 19, 32)            0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 19, 32)            0         \n",
            "_________________________________________________________________\n",
            "batch_norm_1 (BatchNormaliza (None, 19, 32)            128       \n",
            "_________________________________________________________________\n",
            "lstm_2 (LSTM)                (None, 19, 64)            24576     \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_1 (LeakyReLU)    (None, 19, 64)            0         \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 19, 64)            0         \n",
            "_________________________________________________________________\n",
            "batch_norm_2 (BatchNormaliza (None, 19, 64)            256       \n",
            "_________________________________________________________________\n",
            "lstm_3 (LSTM)                (None, 32)                12288     \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 32)                0         \n",
            "_________________________________________________________________\n",
            "batch_norm_3 (BatchNormaliza (None, 32)                128       \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 1)                 33        \n",
            "=================================================================\n",
            "Total params: 45,857\n",
            "Trainable params: 45,537\n",
            "Non-trainable params: 320\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DVbK1WQw8TJk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "4dbf1979-741d-4242-c91b-83e2780beec7"
      },
      "source": [
        "keras.utils.plot_model(model, \"my_hb_model.png\", show_shapes=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 205
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tpk7kL1N-Db8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "24faf118-daeb-4d4c-e60b-8b1a388b695a"
      },
      "source": [
        "plt.plot(pd.DataFrame(history.history))\n",
        "plt.grid(True)\n",
        "#plt.gca().set_ylim(0, 1)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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KTpkE9AiFrN91cU//nqk/sOpyOLQNkl1znDOllJo/ZRHQm6rCuOmlWFhsPbx16g+MdF98YG4zppRS86gsAnpjIgJelOXVa3j00KNTf2DxeZBo1nZ0pVRZKY+AXhUGYEXiAp479hwDuYGTf8CyRrsveu485FAppeZeUQFdRK4UkZ0isktEPjXB+38oIs+KyDYR+ZWIrJ39rE5uOKA3h87FM15xzS6rL4d0LxzQuUaVUuVhyoAuIjZwM/AWYC3wvgkC9neMMecaY9YDNwFfnPWcnkRD3A/oEXc5MSfGoweLaHZZeRmIpc0uSqmyUUwNfSOwyxizxxiTA+4Arh6bwBgzto0jAUzRGXx2ObZFXTxEX8qwoWVDce3o8QZYshF2/mTuM6iUUvPAKSJNO7BvzP5+4JLxiUTkfwCfAMLAmyY6kIjcANwA0NLSQmdn5ylm15dMJk/4bEwK7Hh5P2vPaOKhgYe462d30eA0nPQ4HaE1nLHvNh79yffIxFqmlZf5NFG5K0Wlll3LXVlmXG5jzEkX4N3ArWP2rwO+epL01wL/NtVxL7roIjNdDz744Amv/fY/P2x++58fNi/2vGjW3bbO3PXiXVMfqHu3MZ+rMebhm6edl/k0UbkrRaWWXctdWYopN/CEmSSuFtPkcgDoGLO/JHhtMncAv3nql5aZaaoK053MsqpuFU2xJh459MjUH2pYCc3nwAs/mvsMKqXUHCsmoG8FVovIChEJA9cA945NICKrx+y+DXhp9rJYnMZEhO5UDhFhU+smHjv02MmnpBt29tvh1UcgdWzuM6mUUnNoyoBujCkANwL3ATuAO40xz4vIF0TkqiDZjSLyvIhsw29H/8Cc5XgSjVVh+oby5F2PTa2b6Mn08FJvEdeVNW8H48HOLXOfSaWUmkPF3BTFGLMF2DLutc+O2f7YLOfrlDVWRQDoTeXY1LoJgEcOPsJZDWed/IOLz4W6pbDjR3Dh7851NpVSas6UxZOiAE0Jvy96dypHS6KFlbUri+u+KAJr3gF7HoTs4BznUiml5k7ZBPTmGr+Gfqg/DcClbZfy5JEnybm5qT989tvBzemQukqpklY2AX1VczUAOw75texNrZvIuBm2dW2b+sMdl0C8SXu7KKVKWtkE9NpYiPa6GDsO+Q+tbmjZgC12cc0ulg1r3gov/icUsnOcU6WUmhtlE9ABzm6tGQnoVeEqzlt0Ho8cLKI/Ovjt6LlBePmXc5hDpZSaO2UV0Ne2VvPysRSZvD8k7qbWTTzf/Tz92f6pP7zyjRCuhh0/nONcKqXU3CirgL6mtQbPwItH/Hb0S9suxWB4/PDjU3/YicDqK/z+6DpGulKqBJVVQD+7tQZgpNllXdM6EqFEccPpgt/bJXUU9hVxAVBKqdNMWQX0ZQ1x4mF7pKdLyApxccvFxY3rArDqCrDD2ttFKVWSyiqgW5Zw1uJqth8aHZ59U9sm9g3uY//g/qkPEK2BlZv9dnQzr0O6K6XUjJVVQIfRni4mCMiXtl4KwK8O/Kq4A6x5O/S9Akeem6ssKqXUnCjLgD6YKXCgz39idEXtCs5uOJtvbv8mBa8w9QHOeqs/Nd0ObXZRSpWWsgvoa1uPf2JURPjw+R9m3+A+fri7iC6JVYugYxM8+++QT89lVpVSalaVXUA/a7Hf0+WFMe3omzs2s7ZxLV975mvkvfzUB3n9J6BnD/zgRm1LV0qVjLIL6FURh6UNcXYcHg3oIsJHzv8IB5IHiqulr74CLv8cPPd9eOj/n8PcKqXU7Cm7gA5wdmv1SJPLsDcseQPrGtdxyzO3kHeLqKW/9o/gvPfCz/9S29OVUiWhTAN6DXu7UwzlRm+CiggfXv9hDiQP8IPdP5j6ICLwjq9A+0Vw1w1wWHu9KKVOb2Ub0I2BFw4fX0t/ffvrOa/pvOJr6aEoXPMdiNbCd98HyaNzlGOllJq5ogK6iFwpIjtFZJeIfGqC9z8hIttF5BkReUBEls1+Vou3dtwQAMNEhI+s/wiHUoe4e9fdxR2sejFcczukuuDO66BQxIQZSim1AKYM6CJiAzcDbwHWAu8TkbXjkj0NbDDGnAd8H7hptjN6KpbUx6iKOCcEdIDXtL2G8xedzy3P3FLcbEYA7RfCb/4jvPoI/Pjj2vNFKXVaKqaGvhHYZYzZY4zJAXcAV49NYIx50BgzFOw+CiyZ3WyeGhFhzeITb4wOv/eR9R/hyNAR7nrpruIPuu5d8IZPwtPfhl/83SzmVimlZodTRJp2YN+Y/f3AJSdJ/9+Bn0z0hojcANwA0NLSQmdnZ3G5HCeZTE752RqT5eEDBX7+4INYIse9Z4xhZWQlNz9xM42HGglJqLgTy2s4a/Gbae38G17af4wDS94+rfxPVzHlLleVWnYtd2WZcbmNMSddgHcDt47Zvw746iRpfwe/hh6Z6rgXXXSRma4HH3xwyjS3P/qKWfZnPzKvHEtN+P4jBx8x625bZ769/dundvJC3pjvXmvM52qM+fX3Tu2zM1RMuctVpZZdy11Ziik38ISZJK4W0+RyAOgYs78keO04InI58D+Bq4wxCz4x59nBEADbJ2hHB7hk8SVsaNnALc/cwmDuxKaZSdkOvOtfYfnr4Z4Pw4v3zUZ2lVJqxooJ6FuB1SKyQkTCwDXAvWMTiMgFwNfwg3nX7Gfz1J21uBqRE3u6DBMR/uTiP6E308u/PPsvp3bw4e6MLevgzt+FV4ocb10ppebQlAHdGFMAbgTuA3YAdxpjnheRL4jIVUGyvweqgH8XkW0icu8kh5s38bDDisbEpAEd4JzGc3jHGe/g29u/zb7BfZOmm1C0Bn7nP6B2CXznvXD42RnmWCmlZqaofujGmC3GmDONMWcYY/46eO2zxph7g+3LjTEtxpj1wXLVyY84P85urTluTJeJfOzCj+FYDl968kunfoJEE1x3D0Sq4FvvhKMvTjOnSik1c2X5pOiwNYur2deTZjAz+VOhzfFmfm/d73H/K/fz5JEnT/0kdR1w3d2AgW9cCQefnn6GlVJqBso6oA9PGr3z8Mlvel5/zvW0xFu4aetNeMY79RMtOgt+/z4IJeC2d8DLD00nu0opNSPlHdDbJh4CYLyYE+NjF36M7d3b+dGeaY6s2HgG/Pf7oLYdvv0ueOHH0zuOUkpNU1kH9LbaKDVRh+0TPDE63ttWvo11jev48pNfZig/NGX6CdW0we/9BBavg+9dB9u+M73jKKXUNJR1QBeRkUmjp2KJxSc3fpKudBe3PX/b9E8ab4DfvRdWBP3UH7l5+sdSSqlTUNYBHfx29J2HB3G9qQfUuqD5An5j+W/wjee+weHU4emfNFIF194Ja6+G+z4Dd7wf9nTqoF5KqTlV9gF9bWsN6bzLK92potJ//KKP4xmPm7beVNz8o5NxIvDub8AbPwWvPAzfvBq+ejE8+s+Q7pv+cZVSahJlH9DXBEMATDTy4kTaq9r58PoPc/8r9/M7W36HPX17pn9yy4bLPg2f2AG/9TV/ooyf/hl88Wy496Nw4EmttSulZk3ZB/QzW6qJhiweeqn42YY+eO4H+dLmL3EweZD3/Og93L7j9ul1ZxwWisL518CHHoAbOv2heJ+5E/7lTfCVC+CBv4Qj26d/fKWUogICejRkc/X57fxg20H608U3oVy+7HLuvvpuNi7eyN8+/rf84f1/yJHUkZlnqO0CuPqr8Mc74KqvQv1y+NUX4Z8uhZsvgV/cBF07wJvBBUQpVZHKPqADXHfpMtJ5l7ue2n9Kn2uKNXHzm2/mzzf9OduObuOd976Tn7z8k+GhgmcmVg8XXge/ew/88U546/+BWAM8+Nfwj5vgpuX+cAIP/m946X4Y6pn5OZVSZa2YCS5K3rr2WtZ31PGtR1/h+tcsR8ZNeHEyIsJ7znoPGxdv5DO/+gyf/OUn2bJnC5+55DO0VrXOTgarmmHjh/ylfz/sfhAOPAH7n4Bf/j0EzT0bY+2Q/S1Y83bo2Oi30SulVKAiaugA121axp6jKR7Z3T2tzy+vXc433/JN/viiP+axw49x9Q+u5lvbv0XBK8xuRmuX+DX3d3wZPvxf8Kl98IEfwZs/RybaDI/f4o8Z83/OhB/cCDt/CvnM7OZBKVWSKqKGDvC281r5yx9v51uPvsJrVjVN6xiO5XD9uuu5YvkV/NWjf8VNW2/ih7t/yOde8znOaTxnlnMciFT5DymteD3PuBeyedOFsOtn/tAC238AT38LnBjULYXEIn8EyKrm0e3qVv8iUdPuN/OcwrcTpVRpKbmAvu+FHg495fFo325CUZtw1CEUsUe2qxuiVDdGsZ3jv3xEQzbv3dDBrb96mcP9GRbXRqedh/aqdv7xzf/Ifa/cx989/ndc++NruXbNtXzovA/REG2YaRFPLloD697pL4Uc7H0Idj0AA/shdQyOPO8/xJSZoK97KO4H9tolsGgNLH8dLH+tH+iVUiWv5AJ676EUfS/DU7texUz29KdAVV2EmqYYNU1Rappi1DbHeGt7A7d5e/ju46/y8SvOnFE+RIQrl1/Ja9pew1ee+gq377idO3feydtWvo1rz76WNQ1rZnT8ojhhWPVmfxmvkIPUURg85LfLDxyA/gPQv8/ff/I2eOyfAPHHnln+Bv+bwLLX+P3llVIlp+QC+nmXddAju3njG9+Im/fIZVzyWZd8tkB2qMBgT4aBo2kGujMMHEuzb0cvqb7Rx/g/Soz03fv4/tMD1C9OUN8Sp6E1QUNbguqGKGKdWpNETbiG/7Xpf/G+Ne/jOzu+ww/3/JC7d93NRS0X8f6z389lHZfhWAvwY3bC/siPte2wZMOJ7xey/oNNLz/k1/K33gqP3gx2BM75Tdjw+9BxiTbRKFVCSi6gDxMRnLCNE566p0ch59J/LE1/V5onnu3iZ1sPUJ8pMPhcNy88fGgknRO2qF/sB/e6ljjVDVGq6iNUN0RJ1EVOaMYZ64y6M/jzS/+cj174Ue7ZdQ/ffeG7fKLzE7QmWtncsZl1TetY17iO5bXLseQ0uBftRPza+LLXAH/m31jdv9Vvl//1HfDM96B5rR/Yz3uP1tqVKgFFBXQRuRL4MmADtxpj/nbc+28A/gE4D7jGGPP92c7oTDhhm8a2Khrbqlh2XhNfPnCEwQb47g2vIzuUp+fQEL2HUvQcTNFzOMX+F3rZ+ei4wbkE4jVhErURnLCFE7KwQza2M7xtEYk5RBIOF8UvZ1PHf2NnegcPHrmfzl8/wk+8B/DwiIYjrG5YxZmNZ7J20dmsbzuPlurmU+pKOSdC0ZGbr1z+eXjuP+CJr8OWP4H7PwvnvNN/r+1CaFwF1mlwUVJKHWfKgC4iNnAzcAWwH9gqIvcaY8Y+q/4qcD3wJ3ORydlkW8L7Ny3lpp/uZFfXIKuaq2k9o5bWM46vgeZzLqneLIM9GQZ7MiR7syR7MqT6c7gFv5knnczj5j3cgkc+55EbKuAWjn/CcxVXsIorJszLbmA3z2N4FuN42CEhEgkRiUQQ/ABvjIHgVkE67XHgwUcRAbEEsQTLEkTAsgXLtvy1JSP7IoD432jw/wOR0QtQ3CEcrIf3I4kQ0VXXED3/Ouwj2+DJb8Bzd8G2b/sZidRA23o/uLdfCNVt/s3aaK3/XiimTTVKLYBiaugbgV3GmD0AInIHcDUwEtCNMXuD90riefX3bOjgH+5/iW8/+iqfv2ri7oahsE1dS5y6lvgpHbuQc8kOFcgM5cmmCmSH8rgFg+d5GNfgeQbPNeQLBQ4NHGZ/70GODHTRPdhDNlfAcUM4hRBRJ0o8FCPmxImFYsSdGO5glvrmRiwsjAeeZzDGYIJjeq7x7yu4Bs/18Fzjj/0VPNlqzOgFopD3yKUL5LPuScsTitpEE+8jGr8OJ5zFdgex0/04O7qxtx3D5jHyJkrWS5A1VSPrnBcn5qRYVHWU5oZBmhe5NLeHiDU2QKzOb6u3Q2CHg3UInCiEqyBS7S92CM/1KOS82Xk6V6kyV0xAbwf2jdnfD1wynZOJyA3ADQAtLS10dnZO5zAkk8lpf3bYRc3C9x7fy6Z4F1FnAWqTArFaWF1bz2r8boO9hV72ZvdyIHeAQ4Vuut1uugvdDLjBBB11IAhNThNt4TbaQ+20hdtoC7WRsBKErBAOzkyaRiEAABwKSURBVCk13xhPcPPg5cDNg5sLlqx/39TNubhZl3QOjAueF8W4UYzXgrEMxnVxrAKhcI6QnSNmp6mzDhOWFNlsmJ6hReztWwl7/CaaKquLWmcvgouFBxhEDIKHwSLnxcmaBDkvQdYkyJsYAJbkefWufycWShENDxGNZohE8tgRgxWxsaI2ViSExCJ4kRhuKI5rx3HtaEl/W5iN3/VSpOWennm9KWqMuQW4BWDDhg1m8+bNp3yMIy/v5pdbfsSq9esJx+NEYolgHSccixOOxbDsqW+UVq/o4V3/9Ajd1St5/yXLTjkf8yldSHMweZAfP/xjIu0Rdvbu5MXeF3mm/xkMJ9ZcI3aEiB0hakepDldTH633l0j9yHZLvIVlNctYWrOUiB2Z0/zn0gWO7huka08PXXuqSPW2Y4yHGfmG4QVdUA2RUIE6J0fYzhKxjxGxUjhmiJ6eLK6pJZmN059q4kBfHd4kv74WecIyRMjqIyQZQlaekFMg5Lg4todtGazjFhCxcAlTIIJL2N82ITzjEI1bxKtDxOuixOvixBtrSSyqJ1ZXTbQqgthzdz+hs7OT6fydlDot9/QUE9APAB1j9pcEry2Ivb9+ild/eT+v/vL+SdPYoRChaIxwNEooEiUci1G/uI3mFWf4y/KVXLi0nnXtNfxT527edeESoqHTd1yUmBPjjLozOC9+HpvP3zzy+lB+iJf6XmJ3326SuSQ5L0emkCHrZkfWg7lBejI97OrbRV+mj75s33EXAUFoq2pjec1yltcuZ2XtSi5ovoAz6s6Ytd444ZhD+5n1tJ9ZD5wxrWN0dnby5jG/6MYzpAdzpHv7yfb2kxlIkhlIkRnMkE0WyKWFfDZMPuuQzxnyeSGdF/I5C8+z8MzxizGCI3lsyWNLDoccNkksXPpMNSm3HhcPSAaL/ycguEStJFFrgJidJGYPErLyGMsBcTCWDWJjxMGyhHAoTyRUIBIuEA55REIu4SjY8Wqcqjrs6jrs6kbs6kac2kbIDOGlerHEBG1mrj+2j1hgOX5TlRU0WenYPhWvmIC+FVgtIivwf4uvAa6d01ydxIVvvYp+J8pF69eTSw+RTQ+RG0oF6zT5TJpcNkMu7W/nMxmyQyleee7XbH/owZHj1LYs5l1NS/jpAcM/fm0fv/W6ddQ0LaK6aRHhaGyhindK4qE45y86n/MXnV/0Z1zPpT/Xz6HUIV7pf4W9A3v9pX8vT3c9zVDBnyC7NlLLBYsu4IKWC7iw+ULOaTyHkB2aq6KcMrGEeG2EeG0zLG+em5N4LuRSkOnHpHvJ9/eS6h5gqDfFUF+GdBrSaZt0xiadiZLOVtGd7aDgWkjB/8YhQbMSxsMYIedGyLoRTj6MUg44FCxRnr/naSzyOJLzLzaSw2L03ocwfC4QMdiWiyUutuViWwbb8hAB1zgnLAahOtRHbfgotfYRau2D1Fn7qJZD2DXN0HQmNK0O1sF2rA7w78fkMi7pgRzpwRyhqE1je9XC99iqYFMGdGNMQURuBO7D77b4dWPM8yLyBeAJY8y9InIxcDdQD7xDRP7CGDMng5uEwhHCVdU0LumYOvE4qb5euvbuoevl3SPri/uPYB56krse+veRdNFEFfG6er92H40SCmr6oWiMUDSCZTtYto1t26PbjkMkUUW0uppYVTWx6hqiVdVEq6qxndOnu79t2TREG2iINpww/owxhv2D+3mq6yl/OfIUnfs7AQhbYTqqO1hWs2ykqWZZzTKWVi+lIdpwWgX7WWPZQe+dGqSug3ArhIGZDpRgPEMu65IdypNLF8ilXdxcnkKyHzfZh5sawB0apDCU4siRburqmym4NgXXouBauG7cHy7fDF8oDHgG8G+Eu65/LXJdyHmC6wrGAzsI8o4UiEgGW/yB5QZztRzqXUreDY/kUTCE7DyOZHBMGkeyODyFLY/iSowhr560W41rjv/drq0zrDq3ilWXLqNxRYsG93lWVKQxxmwBtox77bNjtrfiN8Wc1hJ19axYfxEr1l808tqBniRX3/QTXrvY5g8vbmTw2FEGu48y1NdHPpshl8mQ7Okhn834Nf5sFtctYFwX13WLmkJOhpsuhrsPBjuWZSHBhWHs2rJtRAQRy19b/jqVSvHqj7/v3+QLjiNBX0TLsvz01vDiH5/gGH4+/M8JYDkOthPCCYWwQyHsUBg75O8vsm3eYp3N25xzSVdnOJg+xMGhQ3QP9nDs5Zd5KPcEOfJ4Ap7l1wzDEiZuRYlbMWJWlKgVIRyN4VTHCddUE6utIRarIubEiNgRLLGwxT5u7VjOaPu/Ez1uu7fQS3+2n6gTJWyFSzpQiCV+F9HY+D+/RSek7ezs5OJ5aEs2xpAezNN/NE3/0SH6j6bJpQsU8h6FTAF3KEk+NUghnSZq0jTYPcTYRcwcJe4eJFY4SNJt5KWh1/LUQ+fy5EPbqXd+xqqGF1jR3k2iIUG0oRGrdrE/YFx1i79OLNKmoll0+lQdF0h7QxW/9xsX8Pf37eSdi9byxtee+Ed1Mp7n4hVc3EKebCpFenCAdHKQzMh6EM8tBHF/uPug3x5qjMFzC0EXwwKe644sJnjfr335tbDC0S5qGptOOA6A8Ty/a+TwYgyFfAFjvKAmh992bvyujJ5bwM3ncQt5Cvk8bi5HoZDHzecxk8yW1BgsZ01ZR80ES/9xr/Y7LocjHtmwixFwLYMnBmOBKwZzkhiddzx+8MAXyTke+ZDBRGyIhHCiYUKRKNFogkgsRiySIJ6oIR5JEA3FiDkxonaUWMhfR50oIStE2AoTskOELH9xLOe4i8jwYldIsBER4jVh4jXhE57JKIpbgOQR1g4cYOjQfvY8n2LXrihbuy5ja9foP2xEBolax4hZe4hag/4ScYnGLKJVIaLVUaK1VVQf66Irudt/zsK2/EqObVOQOEOFGtKFBEO5KOlMiPQQ5LMulmNh2+KvHQvLEUJhm0RdhKq6CFUNEarqokQSp9gTzPjdjQtZv0tyNl0gF6yzwbMn4Zg/OGA4uFCHh5eoPa+Vj4oP6AAffP0K/v2JffzFvc/z0z96A+GTPOI/nmXZWGEbJxwmEk9Qs2iO2nOZvzv//oXGHblY+esCbqEwsnYLeX/bdRERrOCbhWVZWI6DWBa5dJpUXw+p3l5Sfb0ke7sZ7O0mk0riuqPH81zX33ZdwOD5necxwbYxHrneNJJ3wZ3oYlPAv3iMXkA8MaQcQ6/jkXc8/0LgeOQd419ILINr+9vD+2IEMSBGsAyIAVtsCDtINIQVCxOKx3DiMaKJKpx4jFAiRjgUJWyH/QuFHcaxHCyxsAi+YSH+vljHpRu+qETsCDXhGmojtdRGaok78dL7BmI7I2MHxTs2sm4jrANS/VkOvtRHJpknPZAh0zdApi9FejBDMlXgWAYyAzaFvvGhaC2PPnuyE6aAFDY5YvYAISuLh40b9Ezy7xHYuCaMGXe/wpEc8XASS8BgY8Ty11gYLDxP8DxwPRm5gT79H4sQq3KIV9vEq/wllhBWXthG85mzNEHOGBrQgYhj87l3nMPv3baVb/zXy/zBG6fXE6NciAi242Dj+I3GMzI7P8vhi1khlyM7lPKXVIpcJk0hlyWfyZDPZoOmsQz5rH8zPJNKkR4aJDOUJDs0RD6Txs0EF6S8f0ExhXEPV4mAFSwA+eH3XUZ7uYxOOp53DKmQRybkkg25FGwDAsOXpeChXzyBgu1RsM2YxaMQXGQKtn+RMbYQjcWJRhLk8nluPfAVsCzE9pvTsGW0KW04n/4dUUJ2iJgdI+pE/W8nTnTkm8fw08fFEISmeBMd1R0sqVrC4sTiaQ0yl6iNsHpDy5TpCjmXTCpPJpUn3Z/mmaeeZu3ZazCF4Bur61cqHMkRCw0Rt/qJWT2E891Ipse/eR1UBEabQf0H+obSIZJDYZLpKKl0hMFMnKFMFOO5iJcbs2QRU8CyXCzHYNsET13jD/FBloh3jEjhKGEGiVgpIpLCkgJ5EyPrxcmZYPHiZE0Vaa+WoUwdQ0N1DB6q44hXR8aroaawi+Yz33vKP8+paEAPXLammcvPbuYrD7zE1evbZzReupo7TjiMEw6TqJu9Mdz95io3uFdhnVA79lw3uDgkySaTZFLBkkySGRwgkxokPThIJjnI0OAA+WwWMGOa1ryRbz35dJZCLkchm8W4J39K1xfC7/VSHM8CY/lrTwyu5TEgBleCrxzjyw5kwx6ZsEtmZO2SjnjB2iUTcfFCNm3VbXRUd9CSaBm5sV4frR/Zboo10RBtmFZ3VydsUxW2qaqPwpJqdh+NsGLDzJ8PsYCqYCmKmw+6hJ6kqc0YyA76cw6keyHT7w9X7WahkPG3C5ngWP7Fd+SYksYzWUzbBCOgzgIN6GP8+dvXcsWXfsnf/GQHX77mgoXOjponYlnYJxlszLJtYtU1xKprZvW8bqEw8u2ikMuRz2Up5LIUstmR/eeeeZY1a84K7q2M3m8Zvmfiua6/7boY4400g7n5/PHNY5NcPDzX9S9E/X0M9Q6QSQ5OfKPftijELDKRgww5r7DLzpK1XXIhvzkrF/IYirr01LvU1DfSEm+hOd5MS6KFlngLrYlWf6lqnXbQnxfF9NYSGen9RN3SUz7FXJZcA/oYyxoT/MEbVvJ/f76Lazcu5ZKVjQudJVXGbMfxu7vGE5OmOZjOs/b1m+ctT57rkh4cYGig3w/yfb2k+vtI9fUyFKwzyaT/jSWZJDc0hFc4fl5dtzbMYHOGI3U72Vb1MF3RJGNbexzLYXF8MYsTi4+r5Y99mnlvdi8v9Lwwcp8hbIUJ22HioficP9lcyjSgj/ORzau466kDfOLOX/M/33Y2V56zGOsUJ71QqlRZtk2irv6UmrSG72v0Hj7IwZ07OPjiDg7u3EHdS2nOopFQtJ1oQx1SE6NQ5ZCOufSlMxzrT3LI28kOd4A+d9C/QR3cW3AtAz+c+HzDN5FrwjXURPx1IpTAsZyRHkvFrMN2eOT+QtSOEnH8tYj4PcyCZjMPD4x/IYqPDJYXJ+pET7tvGhrQx4mFbf7vtRfwZ99/ho/c/hTr2mv4099YwxtWN5VezwOl5sHY+xpL1vgPqxlj6D10kIM7t3Pk5d0Mdh9l8NgxBg8cwQwOUAuMdo6sCZbjWSEHCYeQsI2EHAjZeI7giktePPLikmeILP0UvAImb3DzBjtvyOfBKQiWBznHIxvyyIY9siE3WHvH9XAa3Q7O7YFlxF+C7eNuQQTbITuEY4UwIcELW5iQBSG/ZxQhC88CF8/vnisGVzyMwIcu+APetvods/9vMetHLAMXLq3np3/0Bu55+gBf+tmLfODrj7NxRQN/+htncfHyOZ4EWqkyICI0tLXT0NbOusuOnw8gn80w2N1NsqebfDaDm8/5N4pzOQrB9q6dL9C2eLHfaymbHem55ObzwfMTo89RuEGTTzgaI1wXJxyNEYnFCcViOKGQ39Np+LmQ5ACZ3iT5dHqOfwKFk76bk1dg9eyfVQP6JGxLeNdFS3jH+W3csfVVvvLALn77nx/hjWcu4vdft4LXr2rSphilpiEUiY4E+8kMzfEzF54XDLcQPEw3cnHI5/3nKhznuGE9LNseeWJ7lIwca3jcqFwmHYwj5T9ZPnzjevi5DhNsLz13/ZyUSwP6FMKOxe9eupx3X7SEf3v4FW59aA8f+PrjLGuM8/5LlvLbF3VQn5hxZ22l1DyyLH8M/RCz0z05VlU9K8eZqdOrRf80Fg87fHjzGTz86Tfx5WvW01wd4X9veYFL/uYBPnHnNp56tVdn1VFKLSitoZ+iiGNz9fp2rl7fzguHB/j2o69w91MHuOupAyypj3HF2hauWNvCxuUNOHM48YFSSo2nAX0G1iyu4a9+81w+9Zaz+fEzB/nP549w+2Ov8o3/2kttLMSb1zRzxdoWzuuoY3FNFFvb3JVSc0gD+iyoiji89+KlvPfipaSyBR566Sj/uf0IP3+hi7ue9me2cSyhtS5KR32cJfUxltTHaa2N0lwTpbk6QktNlPp4SLtGKqWmTQP6LEtEHK5c18qV61opuB5PvdrH7qNJ9vcOsa8nzf7eITp3HqVrMHvCZ0O2sKgqQlN1hOqoQ000RHXUoTpYH92fp3/bARoSYerjYRqrwjQkwkScyhjiVSl1ciUX0JMP/Yrq22/n0C9+gVg22Nbo2naQcBiJhLEiESQcQaIRfzvYt6IRJBIN1hEkGsUKFkKzW0N2bIuNKxrYuOLEvuuZvMuRgQxdg1m6BrKj24MZupM5BjN5ugayDGYKDGTyDOX8sThuf2HbCcdKhG2iIZuQbeHYQti2Rrcdi7BtEXYsIo5NxPG3Y2F75KJREwtRM2bbv6iEiYdL7tdDqYpWcn+xuX2vEvn1Mww+97w/EUMwOBHBEJsUTt6h/6Rs2w/+sZh/UQiF/SfVQmEkFPIvFiEnGDnNAsufVcgfbtWffWjkwmLb4Nj+xcYanbEIkeCiIUQsiw5LWDr2eJbFcQNfRPzFM4Y9e1+lsaWVdMEjk3NJFzzSOZf0oIfrGVwjuMbgBjOSFQz+vucveQOu6+EaQ86FftdwzAiuWHgyvLb8tWVhh0IkYmES8QhVsTCRsEMo5BBxbMJhh0jIJhK2cQxYuFjGYBsPy3jYxvhP+kVjWLEodiyGFY/563A4GCfcn71HkGBtsIyHZfy1eJ4/SrUxDL56mAM79mCFHZxwCCsUwgqHccIhfypAx8KxLGxLtNlKVayiArqIXAl8GX9O0VuNMX877v0I8E3gIqAbeK8xZu/sZtXXcO21PNPWNulDB8Z1MbkcJpvFy+YwuSwmk/G3sxn/9UwWk80E6yxeJu2nyWQw6WCdyWDy+dEll8Pk83hD6WAWIMDzRmYT8rddTCG4sAQXGOMWgoGxzQmLYaJj+NsThaQ6Y/BE/BhvjP/o9PjjlgEvWADywXoVMDDF58ZfmDwRDIIZ3hYrGKfc/+ma4P3h1yS4eABYxgSTL/vpXMvGG7vYNp5l4YmNsazjts3I9IA+fzc4p+Xnw4gVpLUw1uh0g1hj1zCUyXDPrd8f/aw/j+GJ5xj+f1BpIKg0jN2XYNYsCcYNH36U3bNtXNvBsx0KtuPvWw6WY2NbFpZt4dg2th1sC1jGw/E8LDwsz8Myrv/wTSKBJKqQRBV2IoFUJbDjCWzH9peQjePYwfSH/muO4/jrUAjbsbAdh5xryOTd4ZkWR8ZytwQkuOAbz4OCP/LkyO/+mLKLMFrRCiZdmS1j/14xJqiQWWMqbAtjyoAuIjZwM3AFsB/YKiL3GmO2j0n234FeY8wqEbkG+Dtg9kdvL4LYNhKLQSxGubUsFztjkZkowI8L9qPfaoILkev6F5OCO7pf8OdONQUX3MLoZMTGw3M9coUCmZyLayQIaMFi+bV8N5fHTadxh4Zw0xm8dBovnfYvjkGehqfmG8myJRixcS3xZ4+xBBfh4L59tDYtwrgFJJ/HFAqQz/t5HnMBHZkh2fMw3piLpOchxoz54/dPKMM/G2MwInhIEOgJZrDxjyHBcS23AK6H5RYQ4wcz8VxszyPk5rDyHhgPRi4HIz9xMCDGz4fleSPbw+vhfIwGXP9nLTDuQuONHHI8GT7PmPINv4aAN3xBYPTC5hgXx/OX+VAIlhPvIo3qAF4myC+jRbUnKnSRPATXsnDFxhsO/OMqT8P/aiMXPxj527GCfxMx/m/Gyc5jRPDEv7iP/9vwxCJz/R9y2Uevn3ZZJlNMDX0jsMsYswdARO4ArgbGBvSrgc8H298HvioiYvRJmwUhI7WUk6SZhfNMPujr7Juv6fdON/NZbmMMBN9IvVzOD2TBBdB1PfIFl2yugAt4wUXXxfYv3pZFIVfATSUpDCbxkkncZBIvmcIbSgUzDrnBeO7uyL7xhmck8oLFf6+nt4f6urogYyMZHAmUI9/ELAsPf19GLtSMbnvBBdN1/cULlmB8eDO8GDAyHLuD2v3Yv6Ng2xMLL7ikDp93uHIy9uflX7DdcRdvD8tzEc9jyZK2Ofk3lKliroi8G7jSGPPBYP864BJjzI1j0jwXpNkf7O8O0hwbd6wbgBsAWlpaLrrjjjumlelkMklVVdFzkJSNSi03VG7ZtdyVpZhyX3bZZU8aYyac8mheb4oaY24BbgHYsGGDmW7NQ2trladSy67lriwzLXcxdwkO4DdpDVsSvDZhGhFx8Ic67p52rpRSSp2yYgL6VmC1iKwQkTBwDXDvuDT3Ah8Itt8N/Fzbz5VSan5N2eRijCmIyI3AffjdFr9ujHleRL4APGGMuRf4V+BbIrIL6MEP+koppeZRUW3oxpgtwJZxr312zHYG+O3ZzZpSSqlToeO7KqVUmdCArpRSZUIDulJKlYkpHyyasxOLHAVemebHm4BjU6YqP5Vabqjcsmu5K0sx5V5mjFk00RsLFtBnQkSemOxJqXJWqeWGyi27lruyzLTc2uSilFJlQgO6UkqViVIN6LcsdAYWSKWWGyq37FruyjKjcpdkG7pSSqkTlWoNXSml1Dga0JVSqkyUXEAXkStFZKeI7BKRTy10fuaKiHxdRLqCyUOGX2sQkftF5KVgXb+QeZwLItIhIg+KyHYReV5EPha8XtZlF5GoiDwuIr8Oyv0XwesrROSx4Pf9e8GIp2VHRGwReVpEfhTsl325RWSviDwrIttE5IngtRn9npdUQB8zv+lbgLXA+0Rk7cLmas7cBlw57rVPAQ8YY1YDDwT75aYA/LExZi2wCfgfwb9xuZc9C7zJGHM+sB64UkQ24c/P+yVjzCqgF3/+3nL0MWDHmP1KKfdlxpj1Y/qez+j3vKQCOmPmNzXG5IDh+U3LjjHml/hDEY91NfBvwfa/Ab85r5maB8aYQ8aYp4LtQfw/8nbKvOzGlwx2Q8FigDfhz9MLZVhuABFZArwNuDXYFyqg3JOY0e95qQX0dmDfmP39wWuVosUYcyjYPgy0LGRm5pqILAcuAB6jAsoeNDtsA7qA+4HdQJ8xphAkKdff938APgl4wX4jlVFuA/yniDwZzLcMM/w9n9c5RdXsMcYYESnbPqciUgX8B/BHxpgBGZ59nfItuzHGBdaLSB1wN7BmgbM050Tk7UCXMeZJEdm80PmZZ68zxhwQkWbgfhF5Yeyb0/k9L7UaejHzm5azIyLSChCsuxY4P3NCREL4wfx2Y8xdwcsVUXYAY0wf8CBwKVAXzNML5fn7/lrgKhHZi9+E+ibgy5R/uTHGHAjWXfgX8I3M8Pe81AJ6MfOblrOxc7d+APjBAuZlTgTtp/8K7DDGfHHMW2VddhFZFNTMEZEYcAX+/YMH8efphTIstzHm08aYJcaY5fh/zz83xryfMi+3iCREpHp4G/hvwHPM8Pe85J4UFZG34re5Dc9v+tcLnKU5ISLfBTbjD6d5BPgccA9wJ7AUf+jh9xhjxt84LWki8jrgIeBZRttUP4Pfjl62ZReR8/Bvgtn4Fa07jTFfEJGV+DXXBuBp4HeMMdmFy+ncCZpc/sQY8/ZyL3dQvruDXQf4jjHmr0WkkRn8npdcQFdKKTWxUmtyUUopNQkN6EopVSY0oCulVJnQgK6UUmVCA7pSSpUJDehKKVUmNKArpVSZ+H+HD70p4AAG6wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xp-YfPAuAwDg"
      },
      "source": [
        "# Cross validate on the test set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IfHfr-s9aE1_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2e55e096-119c-4d6e-9cc3-ca966f1e55a1"
      },
      "source": [
        "%time\n",
        "from keras.models import load_model\n",
        "\n",
        "# Load the model\n",
        "model = load_model('/content/drive/My Drive/Colab Notebooks/Thesis/best model/LSTM.best_mse_'+path_end+bs_name+'.hdf5') # Gave an error when loading without 'custom_objects'.. fixed by https://github.com/keras-team/keras/issues/3911\n",
        "\n",
        "# Compile with the same settings as it has been saved with earlier\n",
        "model.compile(loss=\"mean_squared_error\", optimizer=adam, metrics=['RootMeanSquaredError', 'mae'])\n",
        "\n",
        "print('FINISHED')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CPU times: user 4 µs, sys: 0 ns, total: 4 µs\n",
            "Wall time: 8.82 µs\n",
            "FINISHED\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ThaH8zR7Au-A"
      },
      "source": [
        "#predict from test set\n",
        "y_pred = model.predict(X_test)\n",
        "y_true = y_test.reshape(y_test.shape[0], 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NUAtAF-xF651",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bfb27f25-6322-4f6d-9211-5aaadbeac114"
      },
      "source": [
        "model.evaluate(X_test, y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "45/45 [==============================] - 1s 6ms/step - loss: 0.0040 - root_mean_squared_error: 0.0631 - mae: 0.0449\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.004672599490731955, 0.06835641711950302, 0.045974887907505035]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 209
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8AIrGfWxF6GT"
      },
      "source": [
        "# invert scaling for forecast\n",
        "inv_X_test = X_test.reshape((X_test.shape[0], look_back*num_features))\n",
        "inv_y_pred = np.concatenate((inv_X_test[:, -num_features:], y_pred), axis=1)\n",
        "inv_y_pred = scaler.inverse_transform(inv_y_pred)\n",
        "inv_y_pred = inv_y_pred[:,-1]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8VGQIwj8JXpf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b35b156f-bc92-43a3-9903-55385441bf86"
      },
      "source": [
        "inv_y_pred"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([178.26169288,  62.64709917,  89.75245437, ..., 169.36238122,\n",
              "        71.8743255 , 158.60638994])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 211
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tvXCZWadJIWD"
      },
      "source": [
        "# invert scaling for actual\n",
        "inv_y_true = np.concatenate((inv_X_test[:,-num_features:], y_true), axis=1)\n",
        "inv_y_true = scaler.inverse_transform(inv_y_true)\n",
        "inv_y_true = inv_y_true[:,-1]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qg5_Hke-JQwo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7ed909e2-636d-46a6-8f4c-f35861e0ad92"
      },
      "source": [
        "# calculate RMSE\n",
        "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
        "\n",
        "mse = mean_squared_error(inv_y_true, inv_y_pred)\n",
        "rmse = np.sqrt(mean_squared_error(inv_y_true, inv_y_pred))\n",
        "mae = mean_absolute_error(inv_y_true, inv_y_pred)\n",
        "print('Test MSE: %.3f' % mse)\n",
        "print('Test RMSE: %.3f' % rmse)\n",
        "print('Test MAE: %.3f' % mae)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test MSE: 1715.946\n",
            "Test RMSE: 41.424\n",
            "Test MAE: 27.861\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e6Sfvun2KuuO"
      },
      "source": [
        "#for plotting graph\n",
        "\n",
        "x = data[split_index_test:]\n",
        "\n",
        "datetime_difference = len(x) - len(y_true)\n",
        "x = x[datetime_difference:]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1AMA-hgy3PQM"
      },
      "source": [
        "Show in the graph only first 50 points."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_d_Csz7EBAs-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eba2f6a2-c037-487b-a36e-5d5f323825f6"
      },
      "source": [
        "plt.figure(figsize=(20,10))\n",
        "plt.plot(inv_y_true[:50], '.-', color='red', label='Real values', alpha=0.5)\n",
        "plt.plot(inv_y_pred[:50], '.-', color='blue', label='Predicted values', alpha=1)\n",
        "\n",
        "plt.ylabel(r'Driving time [s]', fontsize=14)\n",
        "plt.xlabel('time point', fontsize=14) #TODO: set x values as actual dates\n",
        "\n",
        "plt.xticks(fontsize=10, rotation=90)\n",
        "plt.yticks(fontsize=10)\n",
        "\n",
        "plt.legend(loc='upper left', borderaxespad=0, frameon=False, fontsize=14, markerscale=3)\n",
        "\n",
        "mse_result, rmse_result, mae_result = model.evaluate(X_test, y_test)\n",
        "\n",
        "plt.title(path_name+'\\n LSTM %.0f hidden layers result \\n batch_size = %.0f \\n MSE = %.2f \\n RMASE = %.1f  \\n MAE = %.1f' % (n_layers, bs, mse, rmse, mae), fontsize = 14)\n",
        "\n",
        "print('FINISHED')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "45/45 [==============================] - 0s 6ms/step - loss: 0.0047 - root_mean_squared_error: 0.0684 - mae: 0.0460\n",
            "FINISHED\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ToZ1K7xALIZG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2d936c36-9949-45d6-d1ad-023699f42324"
      },
      "source": [
        "plt.figure(figsize=(20,10))\n",
        "plt.plot( inv_y_true, '.-', color='red', label='Real values', alpha=0.5)\n",
        "plt.plot( inv_y_pred, '.-', color='blue', label='Predicted values', alpha=1)\n",
        "\n",
        "plt.ylabel(r'Driving time [s]', fontsize=14)\n",
        "plt.xlabel('datetime [-]', fontsize=14) #TODO: set x values as actual dates\n",
        "\n",
        "plt.xticks(fontsize=10, rotation=90)\n",
        "plt.yticks(fontsize=10)\n",
        "\n",
        "plt.legend(loc='upper left', borderaxespad=0, frameon=False, fontsize=14, markerscale=3)\n",
        "\n",
        "mse_result, rmse_result, mae_result = model.evaluate(X_test, y_test)\n",
        "\n",
        "plt.title(path_name+'\\n LSTM %.0f hidden layers result \\n batch_size = %.0f \\n MSE = %.2f \\n RMASE = %.1f  \\n MAE = %.1f' % (n_layers, bs, mse, rmse, mae), fontsize = 14)\n",
        "\n",
        "print('FINISHED')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "45/45 [==============================] - 0s 6ms/step - loss: 0.0047 - root_mean_squared_error: 0.0684 - mae: 0.0460\n",
            "FINISHED\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TmJnUXtLMPVD"
      },
      "source": [
        "# Combine results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BshbaVGdZqHj"
      },
      "source": [
        "comb_result= comb_result.drop(['Unnamed: 0'], axis=1) #, 'Unnamed: 0.1', 'Unnamed: 0.1.1'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sOC302HI4sMp"
      },
      "source": [
        "comb_result"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SuBIB81yZ1it",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "75ee251d-3a9e-4a15-f687-15efc5cd3c47"
      },
      "source": [
        "int(datetime_difference)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "19"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 219
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CfSlVampL8wH"
      },
      "source": [
        "comb_result=comb_result[int(datetime_difference):].reset_index(drop=True)\n",
        "#comb_result = comb_result.drop(['level_0'], axis = 1)\n",
        "comb_result"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-yzn9qb5ZHYc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2170e94b-49fe-4ade-b67a-6282820ad07d"
      },
      "source": [
        "len(inv_y_pred[:])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1422"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 221
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h1mAVAfBMOPA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        },
        "outputId": "a6af00d5-c974-43c3-d226-801652915e40"
      },
      "source": [
        "comb_result['LSTM_'+str(n_layers)+'H_'+bs_name] = inv_y_pred[:]\n",
        "comb_result"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date time</th>\n",
              "      <th>Path</th>\n",
              "      <th>Actual</th>\n",
              "      <th>Actual_trip_cum</th>\n",
              "      <th>MLP</th>\n",
              "      <th>MLP_trip_cum</th>\n",
              "      <th>MLP_Kalman</th>\n",
              "      <th>MLP_Kalman_trip_cum</th>\n",
              "      <th>LSTM_3H_64</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-04-23 16:49:00</td>\n",
              "      <td>Lufthansa-Basis (Haupteingang)|Obenhauptstraße</td>\n",
              "      <td>156.0</td>\n",
              "      <td>156</td>\n",
              "      <td>121.648451</td>\n",
              "      <td>121</td>\n",
              "      <td>121.648451</td>\n",
              "      <td>121.648451</td>\n",
              "      <td>178.261693</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2019-04-23 16:51:00</td>\n",
              "      <td>Obenhauptstraße|Alsterkrugchaussee (Mitte)</td>\n",
              "      <td>66.0</td>\n",
              "      <td>222</td>\n",
              "      <td>121.315331</td>\n",
              "      <td>242</td>\n",
              "      <td>138.491105</td>\n",
              "      <td>260.139556</td>\n",
              "      <td>62.647099</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2019-04-23 16:52:00</td>\n",
              "      <td>Alsterkrugchaussee (Mitte)|Moltrechtweg</td>\n",
              "      <td>72.0</td>\n",
              "      <td>294</td>\n",
              "      <td>122.158887</td>\n",
              "      <td>365</td>\n",
              "      <td>109.445701</td>\n",
              "      <td>369.585258</td>\n",
              "      <td>89.752454</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2019-04-23 16:54:00</td>\n",
              "      <td>Moltrechtweg|Brabandstraße</td>\n",
              "      <td>42.0</td>\n",
              "      <td>336</td>\n",
              "      <td>120.377157</td>\n",
              "      <td>485</td>\n",
              "      <td>101.480843</td>\n",
              "      <td>471.066100</td>\n",
              "      <td>73.118218</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2019-04-23 16:55:00</td>\n",
              "      <td>Brabandstraße|Hindenburgstraße</td>\n",
              "      <td>126.0</td>\n",
              "      <td>462</td>\n",
              "      <td>121.039073</td>\n",
              "      <td>606</td>\n",
              "      <td>94.025853</td>\n",
              "      <td>565.091953</td>\n",
              "      <td>100.192943</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1417</th>\n",
              "      <td>2019-04-25 07:41:00</td>\n",
              "      <td>U Habichtstraße|Habichtstraße (Mitte)</td>\n",
              "      <td>54.0</td>\n",
              "      <td>1614</td>\n",
              "      <td>125.380412</td>\n",
              "      <td>1876</td>\n",
              "      <td>118.402453</td>\n",
              "      <td>1783.071842</td>\n",
              "      <td>94.599245</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1418</th>\n",
              "      <td>2019-04-25 07:43:00</td>\n",
              "      <td>Habichtstraße (Mitte)|U Alter Teichweg</td>\n",
              "      <td>252.0</td>\n",
              "      <td>1866</td>\n",
              "      <td>125.380394</td>\n",
              "      <td>2002</td>\n",
              "      <td>114.813404</td>\n",
              "      <td>1897.885246</td>\n",
              "      <td>227.284139</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1419</th>\n",
              "      <td>2019-04-25 07:46:00</td>\n",
              "      <td>U Alter Teichweg|U Straßburger Straße</td>\n",
              "      <td>180.0</td>\n",
              "      <td>2046</td>\n",
              "      <td>124.038584</td>\n",
              "      <td>2126</td>\n",
              "      <td>122.162981</td>\n",
              "      <td>2020.048227</td>\n",
              "      <td>169.362381</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1420</th>\n",
              "      <td>2019-04-25 07:48:00</td>\n",
              "      <td>U Straßburger Straße|Wandsbeker Allee</td>\n",
              "      <td>78.0</td>\n",
              "      <td>2124</td>\n",
              "      <td>125.380412</td>\n",
              "      <td>2251</td>\n",
              "      <td>126.822178</td>\n",
              "      <td>2146.870405</td>\n",
              "      <td>71.874325</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1421</th>\n",
              "      <td>2019-04-25 07:49:00</td>\n",
              "      <td>Wandsbeker Allee|U Wandsbek Markt</td>\n",
              "      <td>186.0</td>\n",
              "      <td>2310</td>\n",
              "      <td>125.380412</td>\n",
              "      <td>2376</td>\n",
              "      <td>124.176707</td>\n",
              "      <td>2271.047112</td>\n",
              "      <td>158.606390</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1422 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                Date time  ...  LSTM_3H_64\n",
              "0     2019-04-23 16:49:00  ...  178.261693\n",
              "1     2019-04-23 16:51:00  ...   62.647099\n",
              "2     2019-04-23 16:52:00  ...   89.752454\n",
              "3     2019-04-23 16:54:00  ...   73.118218\n",
              "4     2019-04-23 16:55:00  ...  100.192943\n",
              "...                   ...  ...         ...\n",
              "1417  2019-04-25 07:41:00  ...   94.599245\n",
              "1418  2019-04-25 07:43:00  ...  227.284139\n",
              "1419  2019-04-25 07:46:00  ...  169.362381\n",
              "1420  2019-04-25 07:48:00  ...   71.874325\n",
              "1421  2019-04-25 07:49:00  ...  158.606390\n",
              "\n",
              "[1422 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 222
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hlL6EP2qNZr1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a17bac58-c52c-4999-eff4-d943bb2f655c"
      },
      "source": [
        "if route == 202:\n",
        "  route_unique = np.array(['Lufthansa-Basis (Haupteingang)|Obenhauptstraße',\n",
        "        'Obenhauptstraße|Alsterkrugchaussee (Mitte)',\n",
        "        'Alsterkrugchaussee (Mitte)|Moltrechtweg',\n",
        "        'Moltrechtweg|Brabandstraße', 'Brabandstraße|Hindenburgstraße',\n",
        "        'Hindenburgstraße|U Alsterdorf', 'U Alsterdorf|Sydneystraße',\n",
        "        'Sydneystraße|Manilabrücke', 'Manilabrücke|Kapstadtring',\n",
        "        'Kapstadtring|S Rübenkamp', 'S Rübenkamp|AK Barmbek', 'AK Barmbek|Hartzloh',\n",
        "        'Hartzloh|Habichtsplatz', 'Habichtsplatz|U Habichtstraße',\n",
        "        'U Habichtstraße|Habichtstraße (Mitte)',\n",
        "        'Habichtstraße (Mitte)|U Alter Teichweg',\n",
        "        'U Alter Teichweg|U Straßburger Straße',\n",
        "        'U Straßburger Straße|Wandsbeker Allee',\n",
        "        'Wandsbeker Allee|U Wandsbek Markt'], dtype= object)\n",
        "else:\n",
        "  route_unique = (comb_result.Path.unique())\n",
        "route_unique"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Lufthansa-Basis (Haupteingang)|Obenhauptstraße',\n",
              "       'Obenhauptstraße|Alsterkrugchaussee (Mitte)',\n",
              "       'Alsterkrugchaussee (Mitte)|Moltrechtweg',\n",
              "       'Moltrechtweg|Brabandstraße', 'Brabandstraße|Hindenburgstraße',\n",
              "       'Hindenburgstraße|U Alsterdorf', 'U Alsterdorf|Sydneystraße',\n",
              "       'Sydneystraße|Manilabrücke', 'Manilabrücke|Kapstadtring',\n",
              "       'Kapstadtring|S Rübenkamp', 'S Rübenkamp|AK Barmbek',\n",
              "       'AK Barmbek|Hartzloh', 'Hartzloh|Habichtsplatz',\n",
              "       'Habichtsplatz|U Habichtstraße',\n",
              "       'U Habichtstraße|Habichtstraße (Mitte)',\n",
              "       'Habichtstraße (Mitte)|U Alter Teichweg',\n",
              "       'U Alter Teichweg|U Straßburger Straße',\n",
              "       'U Straßburger Straße|Wandsbeker Allee',\n",
              "       'Wandsbeker Allee|U Wandsbek Markt'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 223
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TVJ84ivTUeqY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6543cc02-e015-4d64-b8d0-fb41c1f423a4"
      },
      "source": [
        "comb_result['Path']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       Lufthansa-Basis (Haupteingang)|Obenhauptstraße\n",
              "1           Obenhauptstraße|Alsterkrugchaussee (Mitte)\n",
              "2              Alsterkrugchaussee (Mitte)|Moltrechtweg\n",
              "3                           Moltrechtweg|Brabandstraße\n",
              "4                       Brabandstraße|Hindenburgstraße\n",
              "                             ...                      \n",
              "1417             U Habichtstraße|Habichtstraße (Mitte)\n",
              "1418            Habichtstraße (Mitte)|U Alter Teichweg\n",
              "1419             U Alter Teichweg|U Straßburger Straße\n",
              "1420             U Straßburger Straße|Wandsbeker Allee\n",
              "1421                 Wandsbeker Allee|U Wandsbek Markt\n",
              "Name: Path, Length: 1422, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 224
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5T_diThVNgxZ"
      },
      "source": [
        "#add a new column for trip accumulate\n",
        "comb_result['LSTM_'+str(n_layers)+'H_'+bs_name+'_trip_cum'] = 0\n",
        "a = 0\n",
        "for i in range(len(comb_result)):\n",
        "  if comb_result['Path'][i] == route_unique[0]: #the first path\n",
        "    a = comb_result['LSTM_'+str(n_layers)+'H_'+bs_name][i]\n",
        "    comb_result['LSTM_'+str(n_layers)+'H_'+bs_name+'_trip_cum'][i] = a\n",
        "  else:\n",
        "    a += comb_result['LSTM_'+str(n_layers)+'H_'+bs_name][i] \n",
        "    comb_result['LSTM_'+str(n_layers)+'H_'+bs_name+'_trip_cum'][i] = a\n",
        "comb_result[:50]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ELJsgHWK53FB"
      },
      "source": [
        "#comb_result = comb_result[['index','Date time','Path','Actual','Actual_trip_cum','MLP','MLP_trip_cum','MLP_Kalman','MLP_Kalman_trip_cum','MLP_2H_64','MLP_2H_64_trip_cum','LSTM_3H_64','LSTM_3H_64_trip_cum','LSTMGA_2H_32','LSTMGA_2H_32_trip_cum','MLPGA_2H_32','MLPGA_2H_32_trip_cum','LSTMGA_Kalman','LSTMGA_Kalman_trip_cum','MLPGA_Kalman','MLPGA_Kalman_trip_cum']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GKyin5mMmQQW"
      },
      "source": [
        "# Export data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hFZbMxb3lwvu"
      },
      "source": [
        "#path_file = 'combine_result'+path_end+'.csv'\n",
        "#comb_result.to_csv(\"/content/drive/My Drive/Colab Notebooks/Thesis/combine_result_\"+path_end+\".csv\")\n",
        "#comb_result_rmse.to_csv(\"/content/drive/My Drive/Colab Notebooks/Thesis/combine_result_rmse_\"+path_end+\".csv\")\n",
        "#!cp path_file \"/content/drive/My Drive/Colab Notebooks/Thesis\"\n",
        "\n",
        "comb_result.to_csv(\"/content/drive/My Drive/Colab Notebooks/Thesis/combine_result_\"+path_end+\"_m\"+str(month)+\".csv\")\n",
        "#comb_result_rmse.to_csv(\"/content/drive/My Drive/Colab Notebooks/Thesis/combine_result_rmse_\"+path_end+\"_m\"+str(month)+\".csv\")"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}